NDLI: Influential nodes selection: a data reconstruction perspective

Content Provider	ACM Digital Library
Author	Wang, Zhefeng Chen, Enhong Wang, Hao Liu, Qi
Abstract	Influence maximization is the problem of finding a set of seed nodes in social network for maximizing the spread of influence. Traditionally, researchers view influence propagation as a stochastic process and formulate the influence maximization problem as a discrete optimization problem. Thus, most previous works focus on finding efficient and effective heuristic algorithms within the greedy framework. In this paper, we view the influence maximization problem from the perspective of data reconstruction and propose a novel framework named \textsl{Data Reconstruction for Influence Maximization}(DRIM). In our framework, we first construct an influence matrix, each row of which is the influence of a node to other nodes. Then, we select \$k\$ most informative rows to reconstruct the matrix and the corresponding nodes are the seed nodes which could maximize the influence spread. Finally, we evaluate our framework on two real-world data sets, and the results show that DRIM is at least as effective as the traditional greedy algorithm.
Starting Page	879
Ending Page	882
Page Count	4
File Format	PDF
ISBN	9781450322577
DOI	10.1145/2600428.2609464
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-07-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Influence maximization Social networks Data reconstruction
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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Influential nodes selection: a data reconstruction perspective